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| Home > Publications database > Localisation and time courses of CMV generators from MFT analysis of average MEG signals |
| Book/Report | FZJ-2020-00578 |
2000
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
Please use a persistent id in citations: http://hdl.handle.net/2128/24039
Report No.: Juel-3808
Abstract: The research presented here is divided into two parts. The first part addresses questionsrelating to the localisation capability of magnetoencephalography (MEG), with emphasiseon testing the accuracy of typical MEG systems before any source reconstruction isapplied. The second and main part is concerned with localisation and time courses ofneuronal generators contributing to both the slow and fast neuromagnetic field changesassociated with the contingent negative variation (CNV) in the normal human brain. Weuse multi-channel and full head MEG systems to study the magnetic counterpart of theCNV, the contingent magnetic variation (CMV). MEG analysis of such near DC-likesignals requires advanced source reconstruction that is able to identify widely distributed aswell as focal sources which often fire simultaneously from cortical as well as from deeperbrain structures. We use magnetic field tomography (MFT) to extract time courses ofregional brain activity. Results are presented from a multi-subject CMV study performedusing the BTi MAGNES II system (Experiment 1) and a single subject experiment, usingthe CTF whole cortex system (Experiment 2).From Experiment 1 we identified four different CMV generators (auditory cortex, sensorimotorcortex, inferior prefrontal cortex, posterior inferior parietal area) and observedpriming of the auditory cortex as part of the early CMV complex and the priming of thesensorimotor cortex as part of the late CMV complex. These results were confirmed byExperiment 2, where the full head coverage also revealed two additional areas, thesupplementary motor area and the posterior cingulate cortex, which were dramaticallyreduced when identical runs were repeated. The SMA activity has been notoriouslydifficult to identify non-invasively from electrophysiological data, especially from MEG,so our success in identifying them clearly and showing how they change with repetition canbe considered as the highlight of our project.
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